Method and device for generating review article of hot news, and terminal device

ABSTRACT

The present disclosure provides a method and a device for generating a review article of hot news, and a terminal device. By way of example, the method may include: determining hot news based on reading feedback information and publishing time of respective news in a current webpage library; obtaining review data corresponding to the hot news; selecting a target review set from the review data based on a preset high-quality review model; and generating the review article based on the hot news and the target review set corresponding to the hot news.

CROSS REFERENCE TO RELATED APPLICATION

This application is based on and claims priority to Chinese PatentApplication No. 201711444442.5, filed with the State IntellectualProperty Office of P. R. China on Dec. 27, 2017, the entire contents ofwhich are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the field of computer technology, andmore particularly to a method and a device for generating a reviewarticle of hot news, and a terminal device.

BACKGROUND

With the rapid development of information technology and the Internet,online news is becoming more and more popular, and has become a majorway for people to obtain information in their daily lives. People canaccess and browse a variety of multimedia news in the form of pictures,text or video through some major web portals or large news websites.

In the billions of pieces of news information that emerges on theInternet every moment, current hot news is usually the focus ofattention. Moreover, people often want to know other people's opinionson hot news, thus hot news including high-quality reviews is morepopular. Therefore, it is of great significance to generate articles ofhot news with high-quality reviews, to facilitate people to understanddetails of the hot news and public opinions.

At present, review articles of hot news are generally generated bymanually collecting hot news and reviews, and then editing to generatecorresponding articles, which is labor-intensive, costly, and timeconsuming.

SUMMARY

Embodiments of the present disclosure provide methods for generating areview article of hot news. In one embodiment, the method may include:determining hot news based on reading feedback information andpublishing time of respective news in a current webpage library;obtaining review data corresponding to the hot news; selecting a targetreview set from the review data based on a preset high-quality reviewmodel; and generating the review article based on the hot news and thetarget review set corresponding to the hot news.

Embodiments of present disclosure provide a terminal device, including:a memory, a processor and computer programs stored in the memory andexecutable by the processor. The processor is configured to execute thecomputer programs to perform the method for generating a review articleof hot news according to embodiments of the present disclosure.

Embodiments of the present disclosure provide a non-transitory computerreadable storage medium, stored with computer programs thereon that,when executed by a processor, cause the method for generating a reviewarticle of hot news according to embodiments of the present disclosureto be performed.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects and advantages of embodiments of the presentdisclosure will become apparent and more readily appreciated from thefollowing descriptions made with reference to the drawings, in which:

FIG. 1 is a flow chart of a method for generating a review article ofhot news according to an embodiment of the present disclosure.

FIG. 2 is a flow chart of a method for generating a review article ofhot news according to another embodiment of the present disclosure.

FIG. 3 is a block diagram of a device for generating a review article ofhot news according to an embodiment of the present disclosure.

FIG. 4 is a block diagram of a device for generating a review article ofhot news according to another embodiment of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings, in which the sameor similar elements and the elements having same or similar functionsare denoted by like reference numerals throughout the descriptions.Embodiments described herein with reference to drawings are explanatoryand used to generally understand the present disclosure, and shall notbe construed to limit the present disclosure.

In the related art, review articles of hot news are generally generatedby manually collecting hot news and reviews, and then editing togenerate corresponding articles, which is labor-intensive, costly andtime consuming, and user experience is poor. Thus, embodiments of thepresent disclosure provide a method for generating a review article ofhot news.

With the method for generating a review article of hot news according toembodiments of the present disclosure, hot news is determined based onreading feedback information and publishing time of respective news in acurrent webpage library, and then after review data corresponding to thehot news is obtained, a target review set is selected from the reviewdata based on a preset high-quality review model, and the review articleis generated based on the hot news and the target review setcorresponding to the hot news. Thus, the review article of the hot newscan be automatically generated based on the webpage library, the cost ofgenerating articles is reduced, the speed and timeliness of generatingarticles is improved, and user experience is improved.

FIG. 1 is a flow chart of a method for generating a review article ofhot news according to an embodiment of the present disclosure. Asillustrated in FIG. 1, the method may include the following.

At block 101, hot news is determined based on reading feedbackinformation and publishing time of respective news in a current webpagelibrary.

The executive subject of the method for generating a review article ofhot news provided by embodiments of the present disclosure may include adevice for generating a review article of hot news according toembodiments of the present disclosure, and the device can be configuredin any terminal device to generate the review article of hot news. Thewebpage library may include webpage data of any website such as largenews websites, web portals, or the like.

The reading feedback information may include one or more of the numberof clicks of the news, the number of reviews of the news, etc.

In detail, a number threshold of the number of clicks and/or the numberof reviews and a time threshold of the publishing time may be preset,and then the news whose number of clicks and/or number of reviews isgreater than the preset number threshold and publishing time is afterthe preset time threshold can be determined as the hot news.

It should be noted that, the current webpage library of embodiments ofthe present disclosure may be established based on knowledge graphtechnology. The current webpage library may include a news library, areview library, and encyclopedic knowledge. The news library includesnews of the whole network, and the review library includes reviews ofthe whole network.

Keywords of the news may include the title, the publishing time, thesource, the abstract, the main body, the picture, and the like, andkeywords of reviews may include nicknames of reviewers, the review time,the review content, the number of likes, the number of replies, and thelike.

Web crawler can be used when the webpage library is established, andnews from news websites (such as NetEase, Tencent, Sina, Sohu, etc.) canbe continuously crawled. Since some news pages may include user reviews,information (including page turning review information) such as the username, the review content, the review time, the number of likes, thewebpage address and the like can be extracted from the news pages, andthen the news and the reviews from the same address can be associated.

For news and reviews from different addresses, similarities of the newscan be determined based on the keywords of the news, and the news whosematching degrees with the keywords are greater than a threshold (i.e.,the similarity is high) can be placed in the same repeating group, andthen reviews corresponding to the news in the same repeating group canbe aggregated, so as to achieve aggregation of news in the webpagelibrary and aggregation of reviews in the review library. The keywordsof the hot news may be words with the highest term frequency-inversedocument frequency (TF-IDF) extracted from the title and the main bodyof the hot news.

Furthermore, in order to enrich the content of the webpage library,knowledge graph technology can be used to associate entities in the newsto entities in the encyclopedic knowledge, so as to supplement the newslibrary with information such as pictures using the encyclopedicknowledge.

At block 102, review data corresponding to the hot news is obtained.

The review data may include any form of review data such as text,pictures, videos, and the like.

In detail, the review data corresponding to the hot news may be obtainedin the following ways.

In one way, the review data corresponding to the hot news can beobtained based on an address of the hot news.

In detail, since the hot news and the review data corresponding to thehot news may usually appear on the same webpage, the review datacorresponding to the hot news can be obtained from the webpage librarythrough the address of the hot news.

In the other way, the review data of other news whose matching degreeswith the keywords satisfy a threshold can be obtained based on thekeywords of the hot news.

It can be understood that, in addition to the webpage where the hot newsis located, other webpages may also include review data of the hot newsor similar hot news, and keywords of the same or the similar hot newsmay usually be the same. Therefore, the review data corresponding to thehot news can be obtained from the webpage library based on the keywordsof the hot news.

In detail, a matching threshold may be set in advance, and then thekeyword of the hot news can be matched with keywords of respective newsin the webpage library, such that review data of other news whosematching degrees with the keywords satisfy the matching threshold can betaken as the review data corresponding to the hot news.

At block 103, a target review set is selected from the review data basedon a preset high-quality review model.

The High-quality review refers to a review that is representative,unique and novel.

In detail, a large amount of review data may be annotated in advance toobtain high-quality review data, and then based on original review dataand the annotated high-quality review data, and by taking review contentand the number of likes as features and using a Bayesian polynomialmodel, the high-quality review model can be trained. Then, after thereview data corresponding to the hot news is obtained, each review canbe scored using the trained high-quality review model, and the reviewwhose score is greater than a preset score can define the target reviewset.

The original review data may include high-quality review data andlow-quality review data.

It should be noted that, other models such as neural network may also beused when training the high-quality review model, which is not limitedherein.

At block 104, the review article is generated based on the hot news andthe target review set corresponding to the hot news.

In detail, after the hot news and the target review set corresponding tothe hot news are determined, the review article can be constructed basedon a preset review article model.

The review article model is configured to select data from the obtainedhot news and the corresponding target review set, and combine themaccording to a set manner to generate the review article.

In an implementation, the review article may include one or more of anarticle title, a lead, an abstract, a picture, a review, and the like.

The article title, the lead, the abstract, the picture and the reviewcontent of the review article are described below.

Article Title

In detail, the article title may include various forms. For example, thearticle title may only include the title of the hot news, such as “allthe episodes of People's name (a TV serial) are leaked”. Alternatively,the article title may include the title of the hot news and thecorresponding high-quality reviews, such as “all the episodes ofPeople's name are leaked|Netizens: anti-corruption drama is on the roadto corruption”. Alternatively, the article title may include the titleof the hot news and preset general content, such as “all the episodes ofPeople's name are leaked God-like reviews of Netizens are gripping”, orthe like.

It should be noted that, the high-quality review in the article titlemay be the review with the highest score after each review datacorresponding to the hot news is scored using the high-quality reviewmodel. The number of words in the article title can be set as needed.

Lead

In detail, the lead can be located in different positions of thearticle. The lead is configured to connect various parts of the reviewarticle, to make the article more smooth and fluent.

Abstract

In detail, the abstract is configured to briefly introduce the hot news.The abstract can be generated by an abstract generator, and the abstractis usually located before specific content of the hot news.

Picture

In detail, the picture may be the picture in the webpage where the hotnews is located, or may be the picture in the webpage where the hot newswhose matching degree with keywords satisfies a threshold is located. Inaddition, the picture may be displayed behind the abstract, or may beinterspersed between the content of the hot news, which is not limitedherein.

By setting pictures in the review article, visual fatigue of userscaused by a large amount of text can be avoided.

Review Content

In detail, the number of reviews displayed in the review article may bepreset, after the review data corresponding to the hot news is scored byusing the preset high-quality review model to select the target reviewset from the review data, the review data in the target review set canbe sorted from high to low by scores, such that the preset number ofreview data can be selected as the review content of the articlestarting from the review with the highest score.

In an implementation, in order to avoid the single form of the review,the review content may include a short review, a building review and along review. The building review can be displayed in the review articlein the form of picture, and can be obtained by scoring multiplebuildings of the same hot news using the high-quality review model,selecting one building with the highest score, and converting data ofthe building with the highest score into the picture.

It should be noted that, in order to enrich the review content of thereview article, in embodiments of the present disclosure, the reviewdata including different opinions may be selected from the target reviewset to generate the review article. In other words, block 104 mayinclude: determining a user opinion type corresponding to each review inthe target review set; selecting target review data corresponding to areview including at least two types of user opinions from the targetreview set; and generating the review article based on the hot news andthe selected target review data.

Accordingly, at block 104, generating the review article may includeselecting data from the hot news and the selected target review databased on a preset review article model, to construct the review article.

The user opinion type may be classified based on emotion, such as apositive opinion on the hot news or a negative opinion on the hot news.Alternatively, the user opinion type may also be classified based onother factors, which is not limited herein.

In detail, after the user opinion type corresponding to each review inthe target review set is determined, the target review datacorresponding to the review including at least two types of useropinions can be selected from the target review set, and the reviewarticle can be generated based on the hot news and the selected targetreview data.

With the method for generating a review article of hot news according toembodiments of the present disclosure, hot news is determined based onreading feedback information and publishing time of respective news in acurrent webpage library, and then after review data corresponding to thehot news is obtained, a target review set is selected from the reviewdata based on a preset high-quality review model, and the review articleis generated based on the hot news and the target review setcorresponding to the hot news. Thus, the review article of the hot newscan be automatically generated based on the webpage library, the cost ofgenerating articles is reduced, the speed and timeliness of generatingarticles is improved, and user experience is improved.

Based on the above analysis, after the hot news is determined based onthe reading feedback information and the publishing time of respectivenews in the current webpage library, the review data corresponding tothe hot news can be obtained, and then the target review set can beselected from the review data based on the preset high-quality reviewmodel, such that the review article can be generated based on the hotnews and the target review set corresponding to the hot news. In actualapplications, for the same hot news, the hot news may be reported andreprinted by many different websites, thus the hot news determined basedon the reading feedback information and the publishing time ofrespective news in the current webpage library may be duplicated. In thefollowing, the situation will be described and explained withaccompanying FIG. 2.

FIG. 2 is a flow chart of a method for generating a review article ofhot news according to another embodiment of the present disclosure. Asillustrated in FIG. 2, the method may include the following.

At block 201, de-duplicating and merging processing is performed onrespective news in the current webpage library based on keywords of therespective news in a current webpage library.

At block 202, hot news is determined based on reading feedbackinformation and publishing time of respective news in the currentwebpage library.

At block 203, review data corresponding to the hot news is obtained.

In detail, words with the highest TF-IDF can be extracted from the titleand the main body of respective news in the current webpage library asthe keywords of the hot news, and then the news with the same keywordscan be merged into the same news. The review data of the merged news mayinclude review data corresponding to respective news before merging.

For example, it is assumed that news 1 is: Shunkai Yan, an artist inHubei, passed away today, who starred in “The True Story of Ah Q”, news2 is: Shunkai Yan, a famous artist, passed away, who starred in a film“The True Story of Ah Q”, the news 1 corresponds to 32 reviews, and thenews 2 corresponds to 7158 reviews. According to TF-IDF, it can bedetermined that keywords with the biggest weights in both news 1 andnews 2 are: Shunkai Yan, passed away and The True Story of Ah Q, andthen the news 1 and news 2 can be merged into the same news, and themerged news corresponds to 7190 (32+7158=7190) reviews.

It should be noted that, there may be low-quality news (such as clickbait news, reviews without pictures, etc.) in the current webpagelibrary. In embodiments of the present disclosure, the low-quality newscan be deleted to improve the quality of the news in the current webpagelibrary.

In addition, for the implementation process and principle of foregoingblocks 202-203, reference may be made to detailed descriptions offoregoing embodiments, and details are not described herein again.

At block 204, de-duplicating and low-quality filtering processing isperformed on the review data.

It can be understood that, in the review data corresponding to the samehot news, there may be reviews with the same or similar opinions. Inembodiments of the present disclosure, de-duplicating processing may beperformed on the obtained review data.

In an implementation, a message-digest algorithm 5 (MD5) value can begenerated for the remaining part of each review in the review data afterremoving stop words (such as “!”, “#”, “$”, etc.), and then reviews withthe same MD5 values can be determined as the same or similar reviews,thus the obtained review data can be de-duplicated.

In addition, the review data may include low-quality review data such asadvertisements, water army, pornography, reactionary or irrelevantreviews. In embodiments of the present disclosure, low-quality filteringprocessing may be performed on the review data.

In detail, feature recognition may be performed on the review data usinga low-quality recognition model, and low-quality reviews recognized areremoved, so as to achieve low-quality filtering processing on the reviewdata.

The low-quality recognition model may be a random forest regressionmodel, or other types of models, which is not limited herein. Thefeatures may include the length of the review, the number of real words,the number of phone numbers, the number of ad words, and the like.

At block 205, a target review set is selected from the review data basedon a preset high-quality review model.

At block 206, a user opinion type corresponding to each review in thetarget review set is determined.

At block 207, target review data corresponding to a review including atleast two types of user opinions is selected from the target review set.

At block 208, the review article is generated based on the hot news andthe target review set corresponding to the hot news.

The user opinion type may be classified based on emotion, such as apositive opinion on the hot news or a negative opinion on the hot news.Alternatively, the user opinion type may also be classified based onother factors, which is not limited herein.

In detail, after the user opinion type corresponding to each review inthe target review set is determined, the target review datacorresponding to the review including at least two types of useropinions can be selected from the target review set, and the reviewarticle can be generated based on the hot news and the selected targetreview data.

With the method for method for generating a review article of hot newsaccording to embodiments of the present disclosure, de-duplicating andmerging processing is performed on the respective news in the currentwebpage library based on keywords of the respective news in the currentwebpage library, and then the hot news is determined based on thereading feedback information and the publishing time of respective newsin a current webpage library, review data corresponding to the hot newsis obtained, after de-duplicating and low-quality filtering processingis performed on the review data, the target review set is selected fromthe review data based on the preset high-quality review model, useropinion type corresponding to each review in the target review set isdetermined, and target review data corresponding to the review includingat least two types of user opinions from the target review set isselected, the review article is generated based on the hot news and thetarget review set corresponding to the hot news. Thus, the reviewarticle of the hot news can be automatically generated based on thewebpage library, the cost of generating articles is reduced, the speedand timeliness of generating articles is improved, and by performingde-duplicating and merging processing on respective news and the reviewdata corresponding to the news in the current webpage library, thequality of the review articles is improved, and user experience isimproved.

FIG. 3 is a block diagram of a device for generating a review article ofhot news according to an embodiment of the present disclosure. Asillustrated in FIG. 3, the device includes a determining module 31, anobtaining module 32, a selecting module 33 and a generating module 34.

The determining module 31 is configured to determine hot news based onreading feedback information and publishing time of respective news in acurrent webpage library.

The obtaining module 32 is configured to obtain review datacorresponding to the hot news.

The selecting module 33 is configured to select a target review set fromthe review data based on a preset high-quality review model.

The generating module 34 is configured to generate the review articlebased on the hot news and the target review set corresponding to the hotnews.

In detail, the device for generating a review article of hot newsaccording to an embodiment of the present disclosure can perform themethod for generating a review article of hot news according to anembodiment of the present disclosure. The device can be configured inany terminal device to generate the review article of hot news.

In a possible implementation of embodiments of the present disclosure,the obtaining module 32 is configured to obtain the review datacorresponding to the hot news based on an address of the hot news, orobtain review data of other news whose matching degrees with thekeywords satisfy a threshold, based on the keywords of the hot news.

In another possible implementation of embodiments of the presentdisclosure, the generating module 34 is configured to determine a useropinion type corresponding to each review in the target review set,select target review data corresponding to a review including at leasttwo types of user opinions from the target review set, and generate thereview article based on the hot news and the selected target reviewdata.

Furthermore, the generating module 34 is further configured to selectdata from the hot news and the selected target review data based on apreset review article model, to construct the review article.

It should be noted that, explanation and description in embodiments ofthe method for generating a review article of hot news is are alsosuitable for the device for generating a review article of hot news ofthis embodiment, which are not described in detail here.

With the device for generating a review article of hot news according toembodiments of the present disclosure, hot news is determined based onreading feedback information and publishing time of respective news in acurrent webpage library, after review data corresponding to the hot newsis obtained, a target review set is selected from the review data basedon a preset high-quality review model, and then the review article isgenerated based on the hot news and the target review set correspondingto the hot news. Thus, the review article of the hot news can beautomatically generated based on the webpage library, the cost ofgenerating articles is reduced, the speed and timeliness of generatingarticles is improved, and user experience is improved.

FIG. 4 is a block diagram of a device for generating a review article ofhot news according to another embodiment of the present disclosure. Asillustrated in FIG. 4, and on the basis of FIG. 3, the device furtherincludes a first processing module 41 and a second processing module 42.

The first processing module 41 is configured to perform de-duplicatingand merging processing on the respective news in the current webpagelibrary based on keywords of the respective news in the current webpagelibrary.

The second processing module 42 is configured to perform de-duplicatingand low-quality filtering processing on the review data.

It should be noted that, explanation and description in embodiments ofthe method for generating a review article of hot news is are alsosuitable for the device for generating a review article of hot news ofthis embodiment, which are not described in detail here.

With the device for method for generating a review article of hot newsaccording to embodiments of the present disclosure, de-duplicating andmerging processing is performed on the respective news in the currentwebpage library based on keywords of the respective news in the currentwebpage library, and then the hot news is determined based on thereading feedback information and the publishing time of respective newsin a current webpage library, review data corresponding to the hot newsis obtained, after de-duplicating and low-quality filtering processingis performed on the review data, the target review set is selected fromthe review data based on the preset high-quality review model, useropinion type corresponding to each review in the target review set isdetermined, and target review data corresponding to the review includingat least two types of user opinions from the target review set isselected, the review article is generated based on the hot news and thetarget review set corresponding to the hot news. Thus, the reviewarticle of the hot news can be automatically generated based on thewebpage library, the cost of generating articles is reduced, the speedand timeliness of generating articles is improved, and by performingde-duplicating and merging processing on respective news and the reviewdata corresponding to the news in the current webpage library, thequality of the review articles is improved, and user experience isimproved.

Embodiments of the present disclosure further provide a terminal device.The terminal device includes a memory, a processor and computer programsstored in the memory and executable by the processor. The processor isconfigured to execute the computer programs to perform the method forgenerating a review article of hot news according to embodiments of thepresent disclosure is performed.

Embodiments of the present disclosure further provide a computerreadable storage medium configured to store computer programs. When theprograms are executed by a processor, the method for generating a reviewarticle of hot news according to embodiments of the present disclosureis performed.

Embodiments of the present disclosure further provide a computer programproduct stored with instructions thereon. When the instructions areexecuted by a processor, the method for generating a review article ofhot news according to embodiments of the present disclosure isperformed.

In the description of the present disclosure, reference throughout thisspecification to “an embodiment,” “some embodiments,” “example,” “aspecific example,” or “some examples,” means that a particular feature,structure, material, or characteristic described in connection with theembodiment or example is included in at least one embodiment or exampleof the present disclosure. In the specification, the terms mentionedabove are not necessarily referring to the same embodiment or example ofthe present disclosure. Furthermore, the particular features,structures, materials, or characteristics may be combined in anysuitable manner in one or more embodiments or examples. Besides, anydifferent embodiments and examples and any different characteristics ofembodiments and examples may be combined by those skilled in the artwithout contradiction.

In addition, terms such as “first” and “second” are used herein forpurposes of description and are not construed as indicating or implyingrelative importance or significance. Furthermore, in the description ofthe present disclosure, unless specified otherwise, “a plurality of”means two or more than two, such as two or three.

Any procedure or method described in the flow charts or described in anyother way herein may be understood to include one or more modules,portions or parts for storing executable codes that realize particularlogic functions or procedures. Moreover, advantageous embodiments of thepresent disclosure includes other implementations in which the order ofexecution is different from that which is depicted or discussed,including executing functions in a substantially simultaneous manner orin an opposite order according to the related functions, which should beunderstood by those skilled in the art.

The logic and/or step described in other manners herein or shown in theflow chart, for example, a particular sequence table of executableinstructions for realizing the logical function, may be specificallyachieved in any computer readable medium to be used by the instructionexecution system, device or equipment (such as the system based oncomputers, the system including processors or other systems capable ofobtaining the instruction from the instruction execution system, deviceand equipment and executing the instruction), or to be used incombination with the instruction execution system, device and equipment.As to the specification, “the computer readable medium” may be anydevice adaptive for including, storing, communicating, propagating ortransferring programs to be used by or in combination with theinstruction execution system, device or equipment. More specificexamples of the computer readable medium include but are not limited to:an electronic connection (an electronic device) with one or more wires,a portable computer enclosure (a magnetic device), a random accessmemory (RAM), a read only memory (ROM), an erasable programmableread-only memory (EPROM or a flash memory), an optical fiber device anda portable compact disk read-only memory (CDROM). In addition, thecomputer readable medium may even be a paper or other appropriate mediumcapable of printing programs thereon, this is because, for example, thepaper or other appropriate medium may be optically scanned and thenedited, decrypted or processed with other appropriate methods whennecessary to obtain the programs in an electric manner, and then theprograms may be stored in the computer memories.

It should be understood that each part of the present disclosure may berealized by the hardware, software, firmware or their combination. Inthe above embodiments, a plurality of steps or methods may be realizedby the software or firmware stored in the memory and executed by theappropriate instruction execution system. For example, if it is realizedby the hardware, likewise in another embodiment, the steps or methodsmay be realized by one or a combination of the following techniquesknown in the art: a discrete logic circuit having a logic gate circuitfor realizing a logic function of a data signal, an application-specificintegrated circuit having an appropriate combination logic gate circuit,a programmable gate array (PGA), a field programmable gate array (FPGA),etc.

Those skilled in the art shall understand that all or parts of the stepsin the above exemplifying method of the present disclosure may beachieved by commanding the related hardware with programs. The programsmay be stored in a computer readable storage medium, and the programsinclude one or a combination of the steps in the method embodiments ofthe present disclosure when run on a computer.

In addition, each function cell of the embodiments of the presentdisclosure may be integrated in a processing module, or these cells maybe separate physical existence, or two or more cells are integrated in aprocessing module. The integrated module may be realized in a form ofhardware or in a form of software function modules. When the integratedmodule is realized in a form of software function module and is sold orused as a standalone product, the integrated module may be stored in acomputer readable storage medium.

The above mentioned storage medium may be a read only memory, a magneticdisk or an optical disk, etc. Although explanatory embodiments have beenillustrated and described, it would be appreciated by those skilled inthe art that the above embodiments are exemplary and cannot be construedto limit the present disclosure, and changes, modifications,alternatives and varieties can be made in the embodiments by thoseskilled in the art without departing from scope of the presentdisclosure.

What is claimed is:
 1. A method for generating a review article of hotnews, comprising: determining hot news based on reading feedbackinformation and publishing time of respective news in a current webpagelibrary; obtaining review data corresponding to the hot news; selectinga target review set from the review data based on a preset high-qualityreview model; and generating the review article based on the hot newsand the target review set corresponding to the hot news.
 2. The methodaccording to claim 1, wherein before determining the hot news based onthe reading feedback information and the publishing time of respectivenews in the current webpage library, the method further comprises:performing de-duplicating and merging processing on the respective newsin the current webpage library based on keywords of the respective newsin the current webpage library.
 3. The method according to claim 1,wherein obtaining the review data corresponding to the hot newscomprises: obtaining the review data corresponding to the hot news basedon an address of the hot news; or obtaining review data of other newswhose matching degrees with the keywords satisfy a threshold, based onthe keywords of the hot news.
 4. The method according to claim 1,wherein before selecting the target review set from the review databased on the preset high-quality review model, the method furthercomprises: performing de-duplicating and low-quality filteringprocessing on the review data.
 5. The method according to claim 1,wherein generating the review article based on the hot news and thetarget review set corresponding to the hot news comprises: determining auser opinion type corresponding to each review in the target review set;selecting target review data corresponding to a review comprising atleast two types of user opinions from the target review set; andgenerating the review article based on the hot news and the selectedtarget review data.
 6. The method according to claim 5, whereingenerating the review article comprises: selecting data from the hotnews and the selected target review data based on a preset reviewarticle model, to construct the review article.
 7. The method accordingto claim 1, wherein the reading feedback information comprises one ormore of the number of clicks, and the number of reviews; when thereading feedback information comprises the number of clicks, determiningthe hot news based on the reading feedback information and thepublishing time of the respective news in the current webpage librarycomprises: presetting a number threshold of the number of clicks and atime threshold of the publishing time; and determining news whose numberof click is greater than the preset number threshold and publishing timeis after the preset time threshold as the hot news; when the readingfeedback information comprises the number of reviews, determining thehot news based on the reading feedback information and the publishingtime of the respective news in the current webpage library comprises:presetting a number threshold of the number of reviews and a timethreshold of the publishing time; and determining news whose number ofreviews is greater than the preset number threshold and publishing timeis after the preset time threshold as the hot news.
 8. A terminaldevice, comprising: a memory; a processor; and computer programs storedin the memory and executable by the processor, wherein the processor isconfigured to execute the computer programs, so as to: determine hotnews based on reading feedback information and publishing time ofrespective news in a current webpage library; obtain review datacorresponding to the hot news; select a target review set from thereview data based on a preset high-quality review model; and generatethe review article based on the hot news and the target review setcorresponding to the hot news.
 9. The terminal device according to claim8, wherein the processor is further configured to: performde-duplicating and merging processing on the respective news in thecurrent webpage library based on keywords of the respective news in thecurrent webpage library, before determining the hot news based onreading feedback information and publishing time of respective news in acurrent webpage library.
 10. The terminal device according to claim 8,wherein the processor is configured to obtain the review datacorresponding to the hot news by acts of: obtaining the review datacorresponding to the hot news based on an address of the hot news; orobtaining review data of other news whose matching degrees with thekeywords satisfy a threshold, based on the keywords of the hot news. 11.The terminal device according to claim 8, wherein the processor isfurther configured to: perform de-duplicating and low-quality filteringprocessing on the review data, before selecting the target review setfrom the review data based on the preset high-quality review model. 12.The terminal device according to claim 8, wherein the processor isconfigured to generate the review article based on the hot news and thetarget review set corresponding to the hot news by acts of: determininga user opinion type corresponding to each review in the target reviewset; selecting target review data corresponding to a review comprisingat least two types of user opinions from the target review set; andgenerating the review article based on the hot news and the selectedtarget review data.
 13. The terminal device according to claim 12,wherein the processor is configured to generate the review article byact of: selecting data from the hot news and the selected target reviewdata based on a preset review article model, to construct the reviewarticle.
 14. The terminal device according to claim 8, wherein thereading feedback information comprises one or more of the number ofclicks, and the number of reviews; when the reading feedback informationcomprises the number of clicks, the processor is configured to determinethe hot news based on the reading feedback information and thepublishing time of the respective news in the current webpage library byacts of: presetting a number threshold of the number of clicks and atime threshold of the publishing time; and determining news whose numberof click is greater than the preset number threshold and publishing timeis after the preset time threshold as the hot news; when the readingfeedback information comprises the number of reviews, the processor isfurther configured to determine the hot news based on the readingfeedback information and the publishing time of the respective news inthe current webpage library by acts of: presetting a number threshold ofthe number of reviews and a time threshold of the publishing time; anddetermining news whose number of reviews is greater than the presetnumber threshold and publishing time is after the preset time thresholdas the hot news.
 15. A non-transitory computer readable storage medium,stored with computer programs thereon that, when executed by aprocessor, cause a method for generating a review article of hot news tobe performed, the method comprising: determining hot news based onreading feedback information and publishing time of respective news in acurrent webpage library; obtaining review data corresponding to the hotnews; selecting a target review set from the review data based on apreset high-quality review model; and generating the review articlebased on the hot news and the target review set corresponding to the hotnews.
 16. The non-transitory computer readable storage medium accordingto claim 15, wherein before determining the hot news based on thereading feedback information and the publishing time of respective newsin the current webpage library, the method further comprises: performingde-duplicating and merging processing on the respective news in thecurrent webpage library based on keywords of the respective news in thecurrent webpage library.
 17. The non-transitory computer readablestorage medium according to claim 15, wherein obtaining the review datacorresponding to the hot news comprises: obtaining the review datacorresponding to the hot news based on an address of the hot news; orobtaining review data of other news whose matching degrees with thekeywords satisfy a threshold, based on the keywords of the hot news. 18.The non-transitory computer readable storage medium according to claim15, wherein before selecting the target review set from the review databased on the preset high-quality review model, the method furthercomprises: performing de-duplicating and low-quality filteringprocessing on the review data.
 19. The non-transitory computer readablestorage medium according to claim 15, wherein generating the reviewarticle based on the hot news and the target review set corresponding tothe hot news comprises: determining a user opinion type corresponding toeach review in the target review set; selecting target review datacorresponding to a review comprising at least two types of user opinionsfrom the target review set; and generating the review article based onthe hot news and the selected target review data.
 20. The non-transitorycomputer readable storage medium according to claim 19, whereingenerating the review article comprises: selecting data from the hotnews and the selected target review data based on a preset reviewarticle model, to construct the review article.